Due to the increasing concerns about machine learning interpretability, we believe that interpretation could be added to pre-processing steps. Using this library, you will be able to select the most important features from a multidimensional dataset while explaining your decisions!
- To use SHAP-Selection, you will need:
pip install shap-selection
@INPROCEEDINGS{MarcilioJr2020shapselection,
author={W. E. {Marcílio} and D. M. {Eler}},
booktitle={2020 33rd SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)},
title={From explanations to feature selection: assessing SHAP values as feature selection mechanism},
year={2020},
pages={340-347},
doi={10.1109/SIBGRAPI51738.2020.00053}
}
To use SHAP-Selection, you must have a trained model. It works both for classification and regression purposes!
Load a dataset
iris_data = load_iris()
X, y = iris_data.data, iris_data.target
feature_names = np.array(iris_data.feature_names)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
Fit a model
model = cb.CatBoostClassifier(verbose=False)
model.fit(X_train, y_train)
Use SHAP-Selection
from shap_selection import feature_selection
# please, use agnostic = True to use with any model...
# agnostic = False will only work with tree-based models
feature_order = feature_selection.shap_select(model, X_train, X_test, feature_names, agnostic=False)
Please, if you have any questions feel free to contact me at [email protected]